Relationship between MODIS based Aerosol Optical Depth and PM10 over Croatia
This study analyzes the relationship between Aerosol Optical Depth (AOD) obtained from Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) and ground-based PM10 mass concentration distribution over a period of 5 years (2008–2012), and investigates the applicability of satellite AOD data for ground PM10 mapping for the Croatian territory. Many studies have shown that satellite AOD data are correlated to ground-based PM mass concentration. However, the relationship between AOD and PM is not explicit and there are unknowns that cause uncertainties in this relationship.
The relationship between MODIS AOD and ground-based PM10 has been studied on the basis of a large data set where daily averaged PM10 data from the 12 air quality stations across Croatia over the 5 year period are correlated with AODs retrieved from MODIS Terra and Aqua. A database was developed to associate coincident MODIS AOD (independent) and PM10 data (dependent variable). Additional tested independent variables (predictors, estimators) included season, cloud fraction, and meteorological parameters — including temperature, air pressure, relative humidity, wind speed, wind direction, as well as planetary boundary layer height — using meteorological data from WRF (Weather Research and Forecast) model.
It has been found that 1) a univariate linear regression model fails at explaining the data variability well which suggests nonlinearity of the AOD-PM10 relationship, and 2) explanation of data variability can be improved with multivariate linear modeling and a neural network approach, using additional independent variables.
KeywordsMODIS AOD PM10 PM10-AOD relationship aerosol multivariate linear regression artificial neural network Croatia
Unable to display preview. Download preview PDF.
- European Environment Agency. The European environment — state and outlook. Luxembourg: Publications Office of the European Union, 2010, ISBN 978-92-9213-152-4, doi:10.2800/57792 (2010)Google Scholar
- Al-Saadi J., Szykman J., Pierce R. B., Kittaka C., Neil D., Chu D. A., Remer L., Gumley L., Prins E., Weinstock L., MacDonald C., Wayland R., Dimmick F. and Fishman J., Improving national air quality forecasts with satellite aerosol observations. Bulletin of the American Meteorological Society, 2005, 86, 1249–1261, doi:10.1175/BAMS-86-9-1249CrossRefGoogle Scholar
- Chu D.A., Kaufman Y.J., Zibordi G., Chern J.D., Mao J., Li C., Holben B.N., Global monitoring of air pollution over land from the Earth Observing System-Terra Moderate Resolution Imaging Spectrora-diometer (MODIS), J. Geophys. Res., 2003, 108(D21), 4661, doi:10.1029/2002JD003179CrossRefGoogle Scholar
- Levy R.C., Remer L., Tanre D., Matoo S., Kaufman, Y.J., Algorithm for remote sensing of tropospheric aerosol over dark targets from MODIS: collections 005 and 051:Revision 2, 2009. http://modis-atmos.gsfc.nasa.gov/_docs/ATBD_MOD04_C005_rev2.pdf.Google Scholar
- Chu D.A., Kaufman Y.J., Ichoku C., Remer L.A., Tanre D., Holben B.N., Validation of MODIS aerosol optical depth retrieval over land. Geophysical research letter, 2002, 29, 10.1029/2001GL013205Google Scholar
- Skamarock W.C., Klemp J.B., Dudhia J., Gill D.O., Barker D.M., Wang W., Powers J.G., A description of the Advanced Research WRF Version 2, NCAR/TN-468+STR, NCAR TECHNICAL NOTE, 88., 2007Google Scholar
- Chen F., Dudhia J., Coupling an Advanced Land Surface-Hydrology Model with the Penn State-NCAR MM5 Modeling System. Part I: Model Implementation and Sensitivity. Mon. Wea. Rev., 12001, 29, 569–585Google Scholar
- Hong S.Y., Jade J.O., The WRF Single Moment 6 Class Microphysics Sheme (WSM6). Journal of the Korean Meteorological Society, 42,2, 2006, 129–151Google Scholar
- Kain J.S., Fritsch J.M., Convective parameterization for mesoscale models: The Kain-Fritsch scheme. The representation of cumulus convection in numerical models. Meteor. Monogr., 1993, 24, 165–170Google Scholar
- Mallows C.L., Some Comments on CP”, Technometrics, 1973, 15(4), 661–675Google Scholar
- Stevens J., Applied Multivariate Statistics for the Social Sciences. Taylor & Francis, New York, 2002Google Scholar
- Bishop C.M., Neural networks for Pattern Recognition. Cylerdon Press, Oxford, 1995Google Scholar
- ]_Haykin S., Neural network: a Comprehensive Foundation. Prentice Hall, Upper Saddle River, NJ, 1999Google Scholar
- Lyapustin A., Wang Y., Laszlo I., Kahn R., Korkin S., Remer L., Levy R., Reid J.S., Multiangle implementation of atmospheric correction (MAIAC): 2. Aerosol algorithm. J. Geophys. Res., 2011, 116, D03211Google Scholar